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• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_002 for Triple negative breast cancer sample SA1035X4XB02879

  Dataset EGAD00001011238

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0071_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0071_000 for Triple negative breast cancer sample SA1035X5XB03015

  Dataset EGAD00001011239

• Whole exome sequencing of SarBC-01- and UroBC-01-related samples.

  This dataset contains raw FASTQ files from DNA of SarBC-01-related samples (SarBC-01 patient Germline, SarBC-01 patient Urothelial tumor, SarBC-01 patient Sarcomatoid tumor, SarBC-01 Passage 6 organoids, SarBC-01 Passage 20 organoids) and UroBC-01 related samples (UroBC-01 patient Germline, UroBC-01 patient Urothelial tumor, UroBC-01 Passage 19 organoids, UroBC-01 Passage 6 organoids). DNA was extracted from FFPE tissue, using RecoverAll RNA/DNA extraction kit (Invitrogen, Carlsbad, CA, USA, AM1975) followed by incubation with Uracil-DNA Glycosylase, and fresh or flash frozen tissue, using Quick-DNA/RNA Miniprep kit (Zymo Research, Irvine, CA, USA, D7001). Twist Human Core Exome + RefSeq + Mito-Panel kit (Twist Bioscience, 102031) was used for the whole exome capturing. Sequencing was performed on Illumina NovaSeq 6000 using paired-end 100-bp reads.

  Dataset EGAD00001011157

• Intra-tumor heterogeneity and clonal evolution of papillary renal cell carcinoma

  Limited knowledge of intratumor heterogeneity (ITH), and clonal evolution of papillary renal cell carcinoma (pRCC) and rare kidney tumor subtypes hinders understanding of tumorigenesis and therapeutic efficacy. We conducted an integrative genomic and epigenomic ITH analysis and studied clonal evolutionary history of pRCC and rare kidney tumor subtypes. We dissected multiple consecutive samples from the center of the tumor towards the tumor's periphery, a normal sample ~5 cm distant from the tumor, and, where feasible, metastatic regions in the adrenal gland. Specifically, we performed 60X multi-region whole-genome sequencing (mWGS), both genome-wide methylation and SNP array profiling, and deep targeted sequencing (average 500X coverage) of 254 cancer driver genes (PMID:24390350). The study population comprises 39 patients with kidney cancers, including 23 with papillary type 1 (pRCC1), 12 with papillary type 2 (pRCC2), one of which also had areas of clear cell, and one each with collecting duct tumor (cdRCC), renal fibrosarcoma rSRC, mixed pRCC1/pRCC2, and an unclassified renal cancer with mixed features of pRCC2 and cdRCC. Based on DNA sample availability, we conducted whole genome sequencing (WGS) on 124 samples from 29 subjects, deep targeted sequencing on 139 samples from 38 subjects, SNP array genotyping on 101 samples from 38 subjects, and genome-wide methylation profiling on 139 samples from 28 subjects. All assays were performed on tumor, metastasis and normal tissue samples, with the exception of the SNP array genotyping, which was conducted only on tumor samples.

  Study phs001573

• Transcriptomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum

  Background; Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH.MethodsMultiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping were performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors).ResultsOverall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high ITH and were classified discordantly. Proximal tumors showed ITH related to differences in the microenvironment, while ITH within distal tumors were cancer-cell related. This subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05).ConclusionOur transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

  Study EGAS00001004668

• Regulation of T Cell CXCL13 Production

  This study evaluated the signals that regulate production of the B cell chemoattractant CXCL13 by human CD4 T cells. Single-cell and bulk RNA-seq datasets evaluated the gene expression changes induced by TGF-beta and the aryl hydrocarbon receptor (AHR), two signals that regulate CXCL13 production. Bulk ATAC-seq data evaluated the epigenetic changes in T cell stimulated with an AHR agonist or antagonist. Ex vivo bulk RNA-seq data of sorted T cell subsets from blood of patients with lupus and control donors interrogated the gene expression differences in sorted T cell subsets. Ex vivo ATAC-seq data allowed comparison of epigenetic profiles of PD-1hi Tph cells from rheumatoid arthritis synovial fluid and PD-1hi T follicular helper (Tfh) cells from tonsil to PD-1low CD4 T cells from the same samples. Single-cell RNA-seq data of T cells from lupus patients before and after treatment with anifrolumab (anti-IFNAR) allowed interrogation of T cell populations that change with anifrolumab treatment. These datasets allowed for genomic interrogation of changes induced by AHR, TGF-beta, and interferon and their relationship to CXCL13 production.

  Study phs003582

• Non-small cell lung cancer molecular subtypes and vulnerability to immunotherapy treatment combinations

  The phase 3 IMpower150 trial in treatment-naïve patients with metastatic non-small-cell lung cancer (NSCLC) demonstrated significantly longer progression-free (PFS) and overall survival (OS) with first-line atezolizumab (anti-PD-L1)-bevacizumab (anti-VEGF)-carboplatin-paclitaxel (ABCP) than with bevacizumab-carboplatin-paclitaxel (BCP). We characterised four molecular NSCLC subtypes identified by unsupervised clustering of transcriptomes of 564 pre-treatment primary tumour samples from IMpower150 using non-negative matrix factorization (NMF1-4). Each subtype had distinct tumour PD-L1 expression levels, epithelial characteristics, immune composition, and treatment outcomes. Both NMF2 (enriched in tumour proliferation signal, macrophages, and monocytes) and NMF4 (enriched in B-cells and T cells) had elevated tumour PD-L1 expression. Of these two, only NMF4 demonstrated PFS and OS benefits with ABCP versus either BCP or atezolizumab-carboplatin-paclitaxel (ACP). Patients with NMF1 (enriched in basal and squamous-like cells) had improved outcomes on ABCP compared with ACP or BCP; those with NMF3 (enriched in adenocarcinoma signatures) showed similar outcomes among treatments. These insights could help inform individualised first-line treatment for metastatic NSCLC.

  Study EGAS50000001272

• Atezolizumab Plus Chemotherapy With or Without Bevacizumab in Advanced Biliary Tract Cancer: Clinical and Biomarker Data From the Randomized Phase II IMbrave151 Trial

  PURPOSE Biliary tract cancers (BTCs) harbor an immunosuppressed tumor microenvironment (TME) and respond poorly to PD-1/PD-L1 inhibitors. Bevacizumab (anti-VEGF) plus chemotherapy can promote anticancer immunity, augmenting response to PD-L1 inhibition. PATIENTS AND METHODS This randomized, double-blind, proof-of-concept Phase II study enrolled patients (n=162) with previously untreated advanced BTC (IMbrave151; NCT04677504). Patients were randomized 1:1 to receive 3-weekly cycles of atezolizumab (1200 mg) plus bevacizumab (15 mg/kg) or atezolizumab plus placebo until disease progression or unacceptable toxicity. All patients received cisplatin (25 mg/m2) plus gemcitabine (1000 mg/m2; CisGem) on Days 1 and 8 every 3 weeks for up to 8 cycles. Stratification of patients was by disease status, geographic region, and primary tumor location. The primary endpoint was progression-free survival (PFS). No formal hypothesis testing was performed. Exploratory correlative biomarker analysis was undertaken using transcriptome analysis (n=95) and mutation profiling (n=102) on baseline tumor samples. RESULTS Between February and September 2021, 162 patients were enrolled. Median PFS was 8.3 months in the bevacizumab arm and 7.9 months in the placebo

  Study EGAS50000000387

• Whole exome sequencing of a representative cohort of AML

  The Genomic DNA Clean & Concentrator kit (ZYMO Research) was used to remove EDTA from the DNA samples. Sample libraries were prepared using 100 ng of input according to the KAPA HyperPlus Kit (Roche) using Unique Dual Index adapters (Integrated DNA Technologies, Inc.). Exomes were captured using the SeqCap EZ MedExome (Roche Nimblegen) according to SeqCap EZ HyperCap Library v1.0 Guide (Roche) with the xGen Universal blockers – TS Mix (Integrated DNA Technologies, Inc.). The amplified captured sample libraries were paired-end sequenced (2x100 bp) on the Novaseq 6000 platform (Illumina) and aligned to the hg19 reference genome using the Burrows-Wheeler Aligner (BWA)3, v0.7.15-r1140.

  Dataset EGAD00001007580

• Molecular Signatures of DCIS to Invasive Progression for Basal-Like Breast Cancers: An Integrated Mouse Model and Human Tumor Study

  We studied the genomic signatures of progression in breast ductal carcinoma in situ (DCIS) as it progresses towards triple negative invasive breast cancer (TNBC). Single cell RNA sequencing (scRNAseq) was performed on the C3Tag genetically engineered mouse model that forms human breast-like DCIS and TNBC. Bulk RNA sequencing was performed on RNA harvested from archival formalin-fixed, paraffin embedded (FFPE) pairs comprising of estrogen receptor (ER) positive (ER+) and negative (ER-) subsets of DCIS. 

  Study phs002443

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  To elucidate the feature of age-associated helper T (ThA) cells and its contribution to autoimmune diseases, various peripheral blood immune cell subsets from 136 systemic lupus erythematosus (SLE) and 89 healthy controls (HC) were collected (Naive_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, ThA, Naive_CD8, Naive_B, SM_B, USM_B, DN_B, Plasmablast, NK, CD16p_Mono, CL_Mono, Neu, mDC, pDC, TEMRA_CD8, CM_CD8, EM_CD8, NC_Mono, Int_Mono, LDG). RNA-seq was performed with each cell subset samples.

  Study JGAS000627

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  To elucidate the regulation of gene expression in immune cell subsets and its contribution to autoimmune diseases, 24 peripheral blood immune cell subsets from 50 Systemic Sclerosis and 48 Healthy Controls were collected (Naive_CD4, Mem_CD4, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Th1, Th2, Th17, Tfh, NK, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono, Int_Mono, NC_Mono, mDC, pDC). RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

  Study JGAS000296

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  Various peripheral blood immune cell subsets from 89 healthy volunteers and 136 systemic lupus erhythematosus (SLE) donors were collected (Naive_CD4, Mem_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, NK, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono (or CD16n_Mono), CD16p_Mono, Int_Mono, NC_Mono, mDC, pDC, Neu, LDG). 22 SLE donors were analyzed longitudinally. RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

  Study JGAS000486

• EGAD00010000744

  Subset 2 of osteoarthritis cases genotyped on Illumina 610k from the arcOGEN Consortium (http://www.arcogen.org.uk/) with consent for osteoarthritis studies only.

  Dataset EGAD00010000744

• ICGC PCAWG Dataset: PACA-CA_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: PACA-CA.

  Dataset EGAD00001003162

• ICGC PCAWG Dataset: OV-AU_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: OV-AU.

  Dataset EGAD00001003227

• NIHR BioResource Rare Diseases WGS project - Steroid Resistant Nephrotic Syndrome (SRNS) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Steroid Resistant Nephrotic Syndrome (SRNS) Rare Disease domain

  Dataset EGAD00001004518

• NIHR BioResource Rare Diseases WGS project - Cerebral Small Vessel Disease (CSVD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Cerebral Small Vessel Disease (CSVD) Rare Disease domain

  Dataset EGAD00001004513

• NIHR BioResource Rare Diseases WGS project - Hypertrophic Cardiomyopathy (HCM) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Hypertrophic Cardiomyopathy (HCM) Rare Disease domain

  Dataset EGAD00001004514

• NIHR BioResource Rare Diseases WGS project - Intrahepatic Cholestasis of Pregnancy (ICP) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project.Participants from the Intrahepatic Cholestasis of Pregnancy (ICP) Rare Disease domain

  Dataset EGAD00001004515

• NIHR BioResource Rare Diseases WGS project - Neuropathic Pain Disorders (NPD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Neuropathic Pain Disorders (NPD) Rare Disease domain

  Dataset EGAD00001004516

• NIHR BioResource Rare Diseases WGS project - Primary Membranoproliferative Glomerulonephritis (PMG) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Primary Membranoproliferative Glomerulonephritis (PMG) Rare Disease domain

  Dataset EGAD00001004517

• NIHR BioResource Rare Diseases WGS project - Bleeding, Thrombotic and Platelet Disorders (BPD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Bleeding, Thrombotic and Platelet Disorders (BPD) Rare Disease domain

  Dataset EGAD00001004519

• NIHR BioResource Rare Diseases WGS project - Inherited Retinal Disorders (IRD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Inherited Retinal Disorders (IRD) Rare Disease domain

  Dataset EGAD00001004520

• NIHR BioResource Rare Diseases WGS project - Multiple Primary Malignant Tumours (MPMT) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Multiple Primary Malignant Tumours (MPMT) Rare Disease domain

  Dataset EGAD00001004521

• NIHR BioResource Rare Diseases WGS project - Neurological and Developmental Disorders (NDD) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project – Participants from the Neurological and Developmental Disorders (NDD) Rare Disease domain

  Dataset EGAD00001004522

• NIHR BioResource Rare Diseases WGS project - Primary Immune Disorders (PID) Rare Disease domain

  Short read whole genome sequencing (WGS) CRAM files for the NIHR BioResource Rare Diseases WGS project.Participants from the Primary Immune Disorders (PID) Rare Disease domain

  Dataset EGAD00001004523

• ICGC PCAWG Dataset: MALY-DE_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: MALY-DE.

  Dataset EGAD00001002123

• ICGC PCAWG Dataset: EOPC-DE_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: EOPC-DE.

  Dataset EGAD00001002124

• ICGC PCAWG Dataset: BOCA-UK_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: BOCA-UK.

  Dataset EGAD00001002125

• ICGC PCAWG Dataset: PRAD-UK_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: PRAD-UK.

  Dataset EGAD00001002126

• ICGC PCAWG Dataset: PBCA-DE_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: PBCA-DE.

  Dataset EGAD00001002127

• ICGC PCAWG Dataset: PRAD-CA_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: PRAD-CA.

  Dataset EGAD00001002128

• ICGC PCAWG Dataset: BRCA-EU_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: BRCA-EU.

  Dataset EGAD00001002129

• ICGC PCAWG Dataset: CLLE-ES_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: CLLE-ES.

  Dataset EGAD00001002130

• ICGC PCAWG Dataset: RECA-EU_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: RECA-EU.

  Dataset EGAD00001002131

• ICGC PCAWG Dataset: PACA-AU_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: PACA-AU.

  Dataset EGAD00001002132

• ICGC PCAWG Dataset: LIRI-JP_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: LIRI-JP.

  Dataset EGAD00001002155

• ICGC PCAWG Dataset: ESAD-UK_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: ESAD-UK.

  Dataset EGAD00001002156

• ICGC PCAWG Dataset: PAEN-IT_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: PAEN-IT.

  Dataset EGAD00001002153

• ICGC PCAWG Dataset: PAEN-AU_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: PAEN-AU.

  Dataset EGAD00001002154

• ICGC PCAWG Dataset: MELA-AU_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: MELA-AU.

  Dataset EGAD00001002157

• Methylation and nucleosome occupancy studies on cell-free DNA using enzymatic cytosine conversion

  Raw sequencing data (PE, fastq.gz) from NovaSeq 6000 sequencing runs of: 1. Blood-derived cell-free DNA from six healthy controls, enzymatic cytosine conversion (between 1 and 3 replicates each) 2. Urine-derived cell-free DNA from three healthy controls, enzymatic cytosine conversion (between 1 and 3 replicates each) 3. Blood-derived cell-free DNA from six patients with acute or chronic kidney disease with/without other relevant organ dysfunction, enzymatic cytosine conversion (between 1 and 3 replicates each) 4. Urine-derived cell-free DNA from three patients with acute kidney disease, enzymatic cytosine conversion (between 1 and 3 replicates each) 5. Blood-derived cell-free DNA from three healthy controls, bisulfite cytosine conversion (single datasets) 6. Conventional whole-genome sequencing on genomic DNA of two healthy kidney donors for two of the studied patients (single datasets).

  Dataset EGAD00001006072

• Dataset for cancer_of_unknown_primary-RNA

  Rare cancer sequencing data of 58 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008857

• Dataset for bone_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 48 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008866

• Dataset for breast_cancer-EXON

  Rare cancer sequencing data of 94 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008868

• Dataset for cancer_of_unknown_primary-EXON

  Rare cancer sequencing data of 62 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008870

• Dataset for colorectal_cancer-EXON

  Rare cancer sequencing data of 119 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008871

• Dataset for GIST-EXON

  Rare cancer sequencing data of 64 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008875

• Dataset for GIST-WHOLE_GENOME

  Rare cancer sequencing data of 18 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008876

• Dataset for gynecologic_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 38 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008878

• Dataset for head_and_neck_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 56 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008879

• Dataset for liposarcoma-WHOLE_GENOME

  Rare cancer sequencing data of 48 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008888

• Dataset for melanoma-WHOLE_GENOME

  Rare cancer sequencing data of 22 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008889

• Dataset for melanoma-EXON

  Rare cancer sequencing data of 76 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008890

• Dataset for NSCLC-WHOLE_GENOME

  Rare cancer sequencing data of 34 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008894

• RNAseq data

  Bulk RNAseq of sigmoid colon biopsies from healthy volunteers and ulcerative colitis patients. Subjects were treated with Placebo or IL-22Fc at different doses, and biopsies were collected at day 0 and day 30 post treatment and prepared for RNA sequencing.

  Dataset EGAD00001008816

• Fecal 16S UC sequencing data

  Fecal 16S-V4 rRNA gene sequence data from NCT02749630 ulcerative colitis patients. Stool samples were collected at screening as well as on days 29, 43, 64, 85, and 134 processed for 16SV4 rRNA gene sequencing

  Dataset EGAD00001008818

• Fecal 16S HV sequencing data

  Fecal 16S-V4 rRNA gene sequence data from NCT02749630 healthy volunteers. Stool samples were collected at screening as well as on days 29, 43, 64, 85, and 134 processed for 16SV4 rRNA gene sequencing

  Dataset EGAD00001008840

• Fecal WMS HV sequencing data

  Fecal WMS data from NCT02749630 healthy volunteers. Stool samples were collected at screening as well as on days 29, 43, and 64 and prepared for whole metagenomic sequencing.

  Dataset EGAD00001008841

• Dataset for bone_cancer-RNA

  Rare cancer sequencing data of 55 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008848

• Dataset for breast_cancer-RNA

  Rare cancer sequencing data of 59 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008850

• OSTEOMICS_RNA

  RNA sequencing of 79 libraries were prepared, from sample of Osteosarcoma tumors biopsied at diagnosis, with TruSeq Stranded mRNA kit following recommendations: the key steps consist of PolyA mRNA capture with oligo dT beads using 1µg total RNA, fragmentation to approximately 400pb, DNA double strand synthesis, and ligation of Illumina adaptors amplification of the library by PCR for sequencing. Libraries sequencing was performed using Illumina sequencers (NextSeq 500 or Hiseq 2000/2500/4000) in 75 bp paired-end mode.

  Dataset EGAD00001008213

• Spatial multi-omic map of human myocardial infarction

  We applied an integrative single-cell genomics strategy with single nucleus RNA sequencing (snRNA-seq) and single nucleus Assay for Transposase-Accessible Chromatin sequencing (snATAC-seq) together with spatial transcriptomics from the same tissue mapping human cardiac cells in homeostasis and after myocardial infarction (MI) at unprecedented spatial and molecular resolution. We profiled in total 31 samples from 23 patients including four non-transplanted donor hearts as controls and samples from tissues with necrotic tissue areas (ischemic zone, IZ), border zone (BZ), and the non-affected left ventricular myocardium (remote zone, RZ) of patients with acute MI.

  Dataset EGAD00001008952

• cfMethyl-Seq

  cfMethyl-Seq libraries were generated for 479 cfDNA samples and were sequenced with 150 bp paired-end reads.

  Dataset EGAD00001009003

• Organoid Derivation Project - GRCh38 - WGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011093

• Single-cell RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015713

• Multi-organ landscape of therapy-resistant melanoma

  Metastasis and failure of present-day therapies represent the most common causes of mortality in patients with cutaneous melanoma. To identify the underlying genetic and transcriptomic landscapes, here we analyzed multi-organ metastases and tumor-adjacent tissues from eleven rapid autopsies after treatment with MAPK inhibitor (MAPKi) and/or immune checkpoint blockade (ICB) and death due to acquired resistance. Either treatment elicits shared genetic alterations that suggest immune-evasive, cross-therapy resistance mechanisms. Large, non-clustered deletions, inversions, and inter-chromosomal translocations dominate rearrangements. Using 345 therapy-naïve TCGA melanoma and 131 patient-matched melanoma pre-and-post acquired resistance to only either treatment, we performed cross-cohort analyses to identify MAPKi and ICB as respective contributors to gene amplifications and deletions enriched in autopsy versus therapy-naïve tumors. Private/late mutations and structural variants display shifted mutational and rearrangement signatures, with MAPKi specifically selecting for signatures of defective homologous-recombination, mismatch, and base-excision repair. Transcriptomic signatures and cross-talks with tumor-adjacent macroenvironments nominate organ-specific adaptive pathways. An immune-desert, CD8+-macrophage-biased archetype, T-cell exhaustion, and type-2 immunity characterize the immune contexture. This multi-organ analysis of lethal, therapy-resistant melanoma presents preliminary insights with potential to improve therapeutic strategies.

  Study EGAS00001006644

• Organoid Derivation Project - WGS (2024-10-14)

  Sequencing of tissue samples and their derived organoids. This dataset contains a subset of colorectal and colorectal liver metastasis samples.

  Dataset EGAD00001015424

• DAC Admixture histories of São Tomé e Príncipe

  Contacts: Paul Verdu +33144057317 paul.verdu@mnhn.fr CNRS-MNHN-Université de Paris UMR7206 Eco-anthropology Jorge M. Rocha jrocha@cibio.up.pt CIBIO Short description The Data Access Committee for acess to genotype and sample information used in the article "Nested admixture during and after the Trans-Atlantic Slave Trade in the island of Sao Tome" by Ciccarella M et al.2025 ensures that data-access requests comply with the Informed Consent provided by donors, and with ethical and deontological guidelines with respect to privacy. Research sampling protocols followed the Declaration of Helsinki guidelines and written informed consent was obtained from all volunteer participants involved in the sampling in São Tomé e Príncipe. The study of the São Tomé e Príncipe sample was undertaken with the support and permission of the Provincial Government of the Ministry of Health of the Democratic Republic of São Tomé e Príncipe, and the Provincial Government of Príncipe. Research sampling protocols also followed the French laws of scientific research deontology (Loi n° 2016-483 du 20 avril 2016) and were approved by the French ethics committees and CNIL (Declaration n°1972648).

  Dac EGAC50000000437

• Whole-genome sequencing analysis for understanding of the stepwise progression of lung adenocarcinoma

  In this study, we performed long and short read whole-genome sequencing analysis of lung adenocarcinoma from early to advanced stages to identify molecular events associated with the stepwise progression of lung adenocarcinoma. We particularly focused on very early stage of lung adenocarcinoma, such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA). In addition to the whole-genome sequencing analysis, we conducted transcriptome and epigenome analysis, multiplex fluorescence immunostaining. This study would enable us to understand important molecular events of pathogenesis in detail during lung adenocarcinoma progression.

  Study JGAS000570

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90554A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 628 samples; filetype=bam

  Dataset EGAD00001004730

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90554B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 596 samples; filetype=bam

  Dataset EGAD00001004731

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90554C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1735 samples; filetype=bam

  Dataset EGAD00001004732

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90560A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 585 samples; filetype=bam

  Dataset EGAD00001004733

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90560B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 766 samples; filetype=bam

  Dataset EGAD00001004734

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90600C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 2055 samples; filetype=bam

  Dataset EGAD00001004735

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90648B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 620 samples; filetype=bam

  Dataset EGAD00001004736

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90682

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 624 samples; filetype=bam

  Dataset EGAD00001004737

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90685

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 481 samples; filetype=bam

  Dataset EGAD00001004738

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90689A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 378 samples; filetype=bam

  Dataset EGAD00001004739

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90689B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 735 samples; filetype=bam

  Dataset EGAD00001004740

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90689C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 718 samples; filetype=bam

  Dataset EGAD00001004741

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90694A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 493 samples; filetype=bam

  Dataset EGAD00001004742

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90694B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1222 samples; filetype=bam

  Dataset EGAD00001004743

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90706

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 522 samples; filetype=bam

  Dataset EGAD00001004744

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95621B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 488 samples; filetype=bam

  Dataset EGAD00001004745

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95622A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 509 samples; filetype=bam

  Dataset EGAD00001004746

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95624A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 604 samples; filetype=bam

  Dataset EGAD00001004747

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95632B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 626 samples; filetype=bam

  Dataset EGAD00001004748

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95632D

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 635 samples; filetype=bam

  Dataset EGAD00001004749

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95660A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1522 samples; filetype=bam

  Dataset EGAD00001004750

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95664A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 465 samples; filetype=bam

  Dataset EGAD00001004751

• Dataset for bone_cancer-EXON

  Rare cancer sequencing data of 87 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008865

• Dataset for hematopoietic_malignancy-WHOLE_GENOME

  Rare cancer sequencing data of 49 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008881

• Dataset for leiomyosarcoma-EXON

  Rare cancer sequencing data of 96 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008885

• Dataset for leiomyosarcoma-WHOLE_GENOME

  Rare cancer sequencing data of 66 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008887

• Dataset for other_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 49 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008895

• 20210708_EGA_Melanoma_mk2 WGS 570 donors

  Illumina platform whole genome sequencing data for matched tumour-normal DNA samples from 570 melanoma patients

  Dataset EGAD00001008798

• Extensive three-dimensional intratumor proteomic heterogeneity revealed by multiregion sampling in high-grade serous ovarian tumor specimens

  Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten high-grade serous ovarian carcinomas (HGSOCs) and analyzed by mass spectrometry, reverse phase protein arrays, and RNA sequencing. Unsupervised analyses of protein abundance data revealed independent clustering of an enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering driven by overall tumor “purity.” Comparing these data to previously defined prognostic HGSOC molecular subtypes revealed protein and transcript expression from tumor epithelium correlated with the differentiated subtype, whereas stromal proteins (and transcripts) correlated with the mesenchymal subtype. Protein and transcript abundance in the tumor epithelium and stroma exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial tumor microenvironment protein heterogeneity that directly bears on prognostic signatures, biomarker discovery, and cancer pathophysiology and underscore the need to enrich cellular subpopulations for expression profiling.

  Dataset EGAD00001008344